IP Library Granted Patent US 11,282,226
Granted Patent B2
US 11,282,226 · App. 16/634,847 · Granted Mar 22, 2022

Water level measurement device and shoreline extraction method

Inventors: Hideaki Maehara (Tokyo, JP); Mengxiong Wang (Tokyo, JP); Momoyo Hino (Tokyo, JP); Hidetoshi Mishima (Tokyo, JP); Eiji Ueda (Tokyo, JP); Tetsuro Wada (Tokyo, JP); Kenji Taira (Tokyo, JP)
Assignee: Mitsubishi Electric Corporation
G06T7/73G01F23/292G06T7/97G06T2207/20081G06T2207/30181G06T2207/30232
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Quick Facts
Patent No.
US 11,282,226
App. No.
16/634,847
Granted
Mar 22, 2022
Kind
B2
Abstract

An object of the present invention is to provide a water level measurement device and a shoreline extraction method each of which is capable of stably measuring the water level. A pixel selection unit ( 11 ) selects a pixel of interest ( 302 ) from a designated area ( 301 ) designated from a captured image ( 300 ), and an identification image extraction unit ( 12 ) extracts identification images ( 303 ), ( 304 ) each coming in contact with the pixel of interest ( 302 ). The identification unit ( 13 ) calculates an identification strength indicating a degree to which an area corresponding to each of the identification images ( 303 ), ( 304 ) is a water area, on a basis of the result of machine learning related to identification between the water area and a non-water area. The shoreline extraction unit ( 14 ) extracts a shoreline in the captured image from the identification strength of the area corresponding to each of the identification images ( 303 ), ( 304 ), on a basis of the result of machine learning related to the identification between the water area and the non-water area by the learning unit ( 17 ).

Claims (39)

1. A water level measurement device comprising:

processing circuitry to

successively select pixels of interest from an image area designated from a captured image captured by a monitoring camera;

upon selection of each pixel of interest,

extract, as identification images, a plurality of image areas that come in contact with the currently-selected pixel of interest,

calculate an identification strength indicating a degree to which an area corresponding to each of the plurality of identification images corresponding to the currently-selected pixel is a water area, on a basis of a result of machine learning related to identification between the water area and a non-water area, and

calculate an evaluation value of the currently-selected pixel on the basis of the identification strengths of the corresponding plurality of identification images;

extract a subset of the pixels of interest as a shoreline in the captured image on a basis of comparison of the evaluation values calculated for the respective pixels of interest; and

calculate a water level within an image capturing range of the monitoring camera on a basis of the shoreline extracted.

2. The water level measurement device according to claim 1 , wherein the processing circuitry

extracts a learning image from the captured image; and

executes the machine learning related to the identification between the water area and the non-water area by using the learning image extracted.

3. The water level measurement device according to claim 1 , wherein

the monitoring camera is a camera having a function of three-dimensional measurement within the image capturing range, and

the processing circuitry calculates the water level within the image capturing range of the monitoring camera, on a basis of three-dimensional measured data by the monitoring camera.

4. A water level measurement device comprising:

processing circuitry to

successively extract sets of identification images from an image area designated from a captured image captured by a monitoring camera, each set comprising a pair of identification images one of which is directly above the other in the captured image;

upon extraction of each set of identification images,

calculate an identification strength indicating a degree to which an area corresponding to each identification image in the currently-extracted set of identification images is an water's edge, on a basis of a result of machine learning related to identification between a water area, the water's edge and a non-water area, and

calculate an evaluation value for the currently-extracted set of identification images on a basis of the respective identification strengths;

calculate a position of a shoreline in the captured image by comparing the evaluation values of the respective sets of identification images and determining, on a basis of the comparison, the sets in which both identification images come into contact with the shoreline; and

calculate a water level within an image capturing range of the monitoring camera on a basis of a position of the shoreline calculated.

5. The water level measurement device according to claim 4 , wherein the processing circuitry

extracts a learning image from the captured image; and

executes the machine learning related to the identification between the water area, the water's edge and the non-water area by using the learning image extracted.

6. A water level measurement device comprising: processing circuitry to successively extract pairs of identification images from a captured image captured by a monitoring camera, each pair including one identification image that is above the other in the captured image; identify a position of a shoreline in the captured image, on a basis of a result of machine learning related to identification of each of the identification images in the extracted pairs as one of a water area, a waters edge and a non-water area; and calculate a water level within an image capturing range of the monitoring camera on a basis of the position of the shoreline identified.

7. The water level measurement device according to claim 6 , wherein the processing circuitry

extracts a learning image from the captured image; and

executes machine learning related to identification of the water's edge by using the learning image extracted.

8. The water level measurement device according to claim 7 , wherein

the processing circuitry executes the machine learning related to the identification of the water's edge, by using, as a label of teacher data, a position of a shoreline in the learning image extracted.

9. A shoreline extraction method comprising:

selectively selecting pixels of interest from an image area designated from a captured image captured by a monitoring camera;

upon selection of each pixel of interest,

extracting, as identification images, a plurality of image areas that come in contact with the currently-selected pixel of interest;

calculating an identification strength indicating a degree to which an area corresponding to each of the plurality of identification images corresponding to the currently-selected pixel is a water area, on a basis of a result of machine learning related to identification between the water area and a non-water area, and

calculating an evaluation value of the currently-selected pixel on the basis of the identification strengths of the corresponding plurality of identification images; and

extracting a subset of the pixels of interest as a shoreline in the captured image on a basis of comparison of the evaluation values calculated for the respective pixels of interest.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 31, 2020
From: MAEHARA, HIDEAKI; WANG, MENGXIONG; HINO, MOMOYO; MISHIMA, HIDETOSHI; UEDA, EIJI; WADA, TETSURO; TAIRA, KENJI
To: MITSUBISHI ELECTRIC CORPORATION
Reel/Frame 051691/0095 →
Priority Claims (1)
JP JP2017-165718 · Aug 30, 2017 · national
Continuity (1)
Related Publication 20200202556A1 · Jun 25, 2020